2020
DOI: 10.3390/econometrics8010007
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Cross-Validation Model Averaging for Generalized Functional Linear Model

Abstract: Functional data is a common and important type in econometrics and has been easier and easier to collect in the big data era. To improve estimation accuracy and reduce forecast risks with functional data, in this paper, we propose a novel cross-validation model averaging method for generalized functional linear model where the scalar response variable is related to a random function predictor by a link function. We establish asymptotic theoretical result on the optimality of the weights selected by our method … Show more

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Cited by 7 publications
(6 citation statements)
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“…When dealing with highly variable data such as neural recordings, the approach can be useful to get more robust estimates. In this protocol, we suggest averaging cross-validated models that revealed to be the simplest, but valid solution to finally handle unique values ( Zhang and Zou, 2020 ; Jung and Hu, 2015 ).
Figure 3 Neural data recorded from a parietal neuron during a fix-to-reach task in darkness Left: Observed (black line) and estimated (red line) firing rates (non-overlapping 40 ms bins).
…”
Section: Step-by-step Methods Detailsmentioning
confidence: 99%
See 1 more Smart Citation
“…When dealing with highly variable data such as neural recordings, the approach can be useful to get more robust estimates. In this protocol, we suggest averaging cross-validated models that revealed to be the simplest, but valid solution to finally handle unique values ( Zhang and Zou, 2020 ; Jung and Hu, 2015 ).
Figure 3 Neural data recorded from a parietal neuron during a fix-to-reach task in darkness Left: Observed (black line) and estimated (red line) firing rates (non-overlapping 40 ms bins).
…”
Section: Step-by-step Methods Detailsmentioning
confidence: 99%
“…When dealing with highly variable data such as neural recordings, the approach can be useful to get more robust estimates. In this protocol, we suggest averaging cross-validated models that revealed to be the simplest, but valid solution to finally handle unique values ( Zhang and Zou, 2020 ; Jung and Hu, 2015 ).…”
Section: Step-by-step Methods Detailsmentioning
confidence: 99%
“…(2018) proposed optimal model averaging for partially linear FLM based on Mallows-type criterion [48]. , Zhang and Zou (2020) developed a cross-validation model-averaging estimator based on FLM and generalized FLM, respectively [40,41]. In this study, we investigated Mallows-type model averaging for PLFS model.…”
Section: Introductionmentioning
confidence: 99%
“…ingredient (response variable Y ), weight of each tablet (scalar predictor Z 1 ), and hardness of each tablet (scalar predictor Z 2 ), are also provided. The data have already been divided into training (155), validation(40) and test (460) subsets. Here, the spectra from instrument 1 were used.…”
mentioning
confidence: 99%
“…[35] proposed a model averaging method for functionon-function regression models that selects weights by minimizing the Q-fold cross-validation criterion. [37] considered generalized functional linear models and also constructed a crossvalidation model averaging estimator.…”
Section: Introductionmentioning
confidence: 99%